Agent Safety
FreeNot checkedUnified MCP safety server that detects prompt injection (75 patterns), scans LLM outputs for leaked secrets/PII, enforces API cost budgets, and creates signed a
About
Unified MCP safety server that detects prompt injection (75 patterns), scans LLM outputs for leaked secrets/PII, enforces API cost budgets, and creates signed audit trails. Zero ML dependencies, pure Python.
README
PyPI version License: MIT Python 3.10+
MCP server for AI agent safety. One install gives any MCP-compatible AI assistant access to cost guards, prompt injection scanning, and decision tracing.
Works with Claude Code, Cursor, Windsurf, Zed, and any MCP client.
Install
Claude Code (recommended)
claude mcp add agent-safety -- uvx agent-safety-mcp
Manual (any MCP client)
Add to your MCP config:
{
"mcpServers": {
"agent-safety": {
"command": "uvx",
"args": ["agent-safety-mcp"]
}
}
}
From PyPI
pip install agent-safety-mcp
agent-safety-mcp # runs stdio server
Tools
Cost Guard — Budget enforcement for LLM calls
| Tool | What it does |
|---|---|
cost_guard_configure |
Set weekly budget, alert threshold, dry-run mode |
cost_guard_status |
Check current spend vs budget |
cost_guard_check |
Pre-check if a model call is within budget |
cost_guard_record |
Record a completed call's token usage |
cost_guard_models |
List supported models with pricing |
Example: "Check if I can afford a GPT-4o call with 2000 input tokens"
Injection Guard — Prompt injection scanner
| Tool | What it does |
|---|---|
injection_scan |
Scan text for injection patterns (non-blocking) |
injection_check |
Scan + block if injection detected |
injection_patterns |
List all 75 built-in detection patterns across 9 categories |
Example: "Scan this user input for prompt injection: 'ignore previous instructions and...'"
Decision Tracer — Agent decision logging
| Tool | What it does |
|---|---|
trace_start |
Start a new trace session |
trace_step |
Log a decision step with context |
trace_summary |
Get session summary (steps, errors, timing) |
trace_save |
Save trace to JSON + Markdown files |
Example: "Start a trace for my analysis agent, then log each decision step"
What this wraps
This MCP server wraps the AI Agent Infrastructure Stack — three standalone Python libraries:
- ai-cost-guard —
pip install ai-cost-guard - ai-injection-guard —
pip install ai-injection-guard - ai-decision-tracer —
pip install ai-decision-tracer
All three: MIT licensed, zero runtime dependencies (individually), pure Python stdlib.
The MCP server adds mcp>=1.0.0 as a dependency for the protocol layer.
Why
AI coding assistants (Claude Code, Cursor, etc.) can now protect the agents they help build — checking budgets, scanning inputs, and tracing decisions — without leaving the IDE.
Built from 8 months of running autonomous AI trading agents in live financial markets.
License
MIT
Install Agent Safety in Claude Desktop, Claude Code & Cursor
unyly install agent-safety-mcpInstalls into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.
First time? Get the CLI: curl -fsSL https://unyly.org/install | sh
Or configure manually
Run in your terminal:
claude mcp add agent-safety-mcp -- uvx agent-safety-mcpStep-by-step: how to install Agent Safety
FAQ
Is Agent Safety MCP free?
Yes, Agent Safety MCP is free — one-click install via Unyly at no cost.
Does Agent Safety need an API key?
No, Agent Safety runs without API keys or environment variables.
Is Agent Safety hosted or self-hosted?
A hosted option is available: Unyly runs the server in the cloud, no local setup required.
How do I install Agent Safety in Claude Desktop, Claude Code or Cursor?
Open Agent Safety on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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